Machine Learning Engineer

The Ladders
United States
1 day ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Compensation
$155,520.0 - $228,700.0
Working hours
Regular working hours
Job source

Tech stack

Java (Programming Language) Artificial Intelligence Airflow Cloud Computing Python (Programming Language) Machine Learning Software Engineering SQL Databases Containerization Kubernetes Machine Learning Operations Data Pipelines
+2 more
Docker Service Stack

Job description

This role will focus on building and operating production-grade machine learning systems that improve reliability, scalability, and business impact across the technology stack. You will partner closely with cross-functional stakeholders to translate ambiguous problem statements into practical ML solutions and measurable outcomes. The position also requires strong collaboration with engineering and operations teams to deliver compliant services, strengthen monitoring, and support continuous improvement across the model lifecycle., * Partner with stakeholders to analyze and clarify ML problem statements

  • Design and maintain scalable ML solutions in a production environment
  • Create reproducible ML workflows with modern orchestration tools
  • Implement frameworks to monitor and evaluate model performance and data quality
  • Collaborate on delivering compliant ML-powered services across disciplines
  • Demonstrate system design rationale and understanding of end-to-end models
  • Drive operational excellence, including incident response and customer feedback handling

Requirements

  • 5+ years of experience in building and operating ML systems in production environments
  • Strong foundation in ML/AI concepts such as statistics and optimization applied to real-world challenges
  • Proficient in Python, Java, and SQL, with solid software engineering principles
  • Hands-on experience with data pipelines and cloud platforms, including orchestration tools like Airflow or Kubeflow
  • Familiarity with MLOps tooling and machine learning lifecycle best practices
  • Knowledge of containerization and cloud infrastructure, such as Docker and Kubernetes
  • Excellent communication skills for conveying complex technical concepts clearly

Benefits & conditions

  • Competitive pay and generous time off policies
  • Parental and wellness leave options
  • Comprehensive healthcare benefits
  • Retirement savings program
  • Opportunities for professional development and volunteering

Our client is an equal opportunity employer. We encourage you to apply even if you don’t meet every qualification-your background could be exactly what this team needs.

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